Py-MS data analysis with univariate statistical techniques

It can be clearly seen that the samples are separated into two groups, only one group containing amino-sugars. This type of plot was reported to indicate differences between 

Py-MS analyses not requiring a long time to complete can be rather easily repeated, thereby generating more than one result for a given sample (specimen). The results are commonly affected by fluctuation, and statistical data analysis is frequently needed. 

The usefulness of univariate data analysis seems to be limited when analyzing Py-MS results because in each spectrum there is a significant number of peaks, each representing a measurement. However, if one mass peak is properly selected from the Py-MS spectrum, this can be used as a unique measurement for the given specimen, and univariate data analysis can be quite informative. Also, any peak can be selected separately, one at a time, for evaluation. This type of approach is less informative than multivariate data analysis, but has the advantage of being simpler. 

Before discussing some applications, a few basic aspects on univariate statistics will be presented. A large amount of information exists regarding this field, and more details can be found in the original literature (e.g. [70,71]). Also a variety of computer packages performing statistical data analysis is available (e.g. [71a]). 

The typical approach for the study of errors is to classify them as systematic (or determinate) or random. Systematic errors are generated by a specific cause, and it is assumed that by removing the cause, the systematic error is also eliminated. 

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Py-MS data analysis with univariate statistical techniques. 

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